class_docstr: Upon initialization, this will create num_triplets triplets based on
  the labels provided in labels_to_indices. This is for experimental purposes,
  to see how algorithms perform when the only ground truth is a set of
  triplets, rather than having explicit labels.
code: def __init__(self, labels, num_triplets):
      if isinstance(labels, torch.Tensor):
          labels = labels.numpy()
      self.labels_to_indices = c_f.get_labels_to_indices(labels)
      self.num_triplets = int(num_triplets)
      self.create_fixed_set_of_triplets()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/samplers/fixed_set_of_triplets.py
imports: import numpy
  import torch
  from torch.utils.data.sampler import Sampler
  from ..utils import common_functions
label_desc: Use image node_id 1 to create a new FixedSetOfTriplets object from inherited base classes: Sampler with example: obj = FixedSetOfTriplets(labels, num_triplets)